The Microgrid allows for more efficient and lower-cost power provisions, and is therefore more flexible than traditional power ecosystems. However, the increasingly integrated nature of these systems into network and internet-connected IT systems also potentially makes them susceptible to cyber-attack. This paper examined different challenges related to the cyber attacks threatening wireless microgrid systems from the experimental view. Wireless communication and transmission methods are widely used for secondary control of energy re-sources. However, there are risks of cyber attacks during the communication process, such as Denial-of-Service (DoS) attacks. This paper reports the investigation of potential cyber attacks on wireless communications of microgrid systems. Furthermore, this paper evaluates the practical impacts of cybersecurity breaches targeting microgrid systems, with special attention to those in Australia. In brief, the main goal of this paper is to enhance the mitigation countermeasures for cyber attacks linked to wireless microgrid systems, thus guaranteeing reliable wireless communications within these systems.

Electricity outages can result in consequences for customers and cause disruptions that result in revenue loss, business productivity reduction, appliance damage, and compromised public safety. To minimize outage duration, locating faults quickly and accurately is crucial. Advanced fault management strategies leverage computational intelligence and deploy fault indicators (FIs) and sectionalizing switches (SSs) to locate faults and isolate faulty parts. Although FIs and SSs can improve distribution network reliability, deploying them at every node is impractical owing to cost and complexity. Therefore, it is important to optimize their placement in strategic locations. This research proposes a new optimization problem for the simultaneous placement of FIs and SSs. The aim is to minimize the cost of equipment placement, dispatching field crews, and service interruption. To solve the proposed formulation, the Backtracking Search Algorithm (BSA), a population-based meta-heuristic method, is used to identify optimal nodes for FIs and SSs placement. The performance of the BSA is evaluated through statistical tests, and the proposed strategy is shown to be effective compared to state-of-the-art methods. The results reveal a significant 7% reduction in device requirement, leading to a substantial 6.69% decrease in equipment expenditures and an overall decline in system costs compared to the established approach implemented in the test system. This research highlights the importance of optimization strategies for fault management systems and demonstrates the potential of meta-heuristic algorithms for solving complex problems.

This paper proposes and experimentally implements new phase-locked loop algorithms using non-adaptive and adaptive terminal sliding modes for the three-phase grid-connected inverters. The proposed algorithms aim to extract the grid voltage phase and frequency and provide them for the inverter controller. The proposed sliding mode-based PLL algorithms provide faster, more robust, and more accurate detection of the grid voltage information compared to the conventional PI-PLL. This leads to better current controller performance and subsequently, improves the quality of the injected currents into the grid. The finite-time stability notion is incorporated with robust sliding modes to ensure a fast convergence rate. An adaptive law is proposed to determine a proper value for the switching gain which leads to a better overall performance of the PLL compared with a non-adaptive one. The Lyapunov stability theory is used to obtain the stability proofs of the closed-loop systems under both designed sliding modes-based PLLs. The proposed PLL algorithms are validated by simulation and experimental results for the three-phase grid-connected inverter under normal and various abnormal grid conditions.

With the appearance of electric energy market deregulation, there exists a growing concern over the potential privacy leakage of commercial data among competing power companies where data sharing is essential in the applications such as smart grid state estimation. Most of the existing solutions are either perturbation-based or conventional cryptography-based where a trusted central 3rd party would often be required. This paper proposes privacy-preserving state estimation protocols for DC and AC models. The proposed idea is to distribute the overall task of the system state estimation into sub-tasks which can be performed by local sub-grid operators with their private data. A masking method is designed inside a homomorphic encryption scheme which is then used to ensure both the input and output data privacy during the collaboration process among individual sub-task players. Security is achieved via the computationally indistinguishable post-quantum security guaranteed by a levelled homomorphic encryption scheme over real numbers and the differential privacy of the output estimated states provided by the Laplace mechanism perturbation integrated into the masking linear transformation. Simulation results are presented to demonstrate the validity of our proposed privacy-preserving system state estimation protocols.

Federated Learning is a prominent machine learning paradigm which helps tackle data privacy issues by allowing clients to store their raw data locally and transfer only their local model parameters to an aggregator server to collaboratively train a shared global model. However, federated learning is vulnerable to inference attacks from dishonest aggregators who can infer information about clients’ training data from their model parameters. To deal with this issue, most of the proposed schemes in literature either require a non-colluded server setting, a trusted third-party to compute master secret keys or a secure multiparty computation protocol which is still inefficient over multiple iterations of computing an aggregation model. In this work, we propose an efficient cross-silo federated learning scheme with strong privacy preservation. By designing a double-layer encryption scheme which has no requirement to compute discrete logarithm, utilizing secret sharing only at the establishment phase and in the iterations when parties rejoin, and accelerating the computation performance via parallel computing, we achieve an efficient privacy-preserving federated learning protocol, which also allows clients to dropout and rejoin during the training process. The proposed scheme is demonstrated theoretically and empirically to provide provable privacy against an honest-but-curious aggregator server and simultaneously achieve desirable model utilities. The scheme is applied to false data injection attack detection (FDIA) in smart grids. This is a more secure cross-silo FDIA federated learning resilient to the local private data inference attacks than the existing works.

This article proposes a novel framework to improve the prediction accuracy of very short-term (5-min) wind power generation. The framework consists of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), monarch butterfly optimization (MBO) and long short-term memory (LSTM), called CEMOLS. The CEEMDAN is employed to extract complex hidden features of time-series data into intrinsic mode functions that are predicted using LSTM models with dropout regularization to retain long-term relationships between input and output data, while the optimization algorithm tunes the hyperparameters of the forecasting model. Data from four real wind farms in New South Wales are collected and preprocessed to train and test the forecasting models. Recently developed rival models are compared to identify the best-performing prediction model. The analysis demonstrates that the proposed CEMOLS with low computation time can improve forecasting accuracy on average by 32.96% in mean absolute error, 47.10% in root mean square error and 32.33% in mean absolute percentage error as compared to the benchmark Persistence model. It also demonstrates that sensitive and statistical analysis needs to be carried out to determine robust prediction models among rival models for practical application.

Summary The evolution of the traditional power system toward the modern smart grid has posed many new cybersecurity challenges to this critical infrastructure. One of the most dangerous cybersecurity threats is the false data injection (FDI) attack, especially when it is capable of completely bypassing the widely deployed bad data detector of state estimation (SE) and interrupting the normal operation of the power system. Most of the simulated FDI attacks are designed using a simplified linearized DC model, while most of the industry‐standard SE systems are based on the nonlinear AC model. In this article, a comprehensive FDI attack scheme is presented based on the nonlinear AC model. A case study of the nine‐bus Western System Coordinated Council (WSCC)'s power system is provided, using an industry‐standard package to assess the outcomes of the proposed design scheme. A public FDI dataset is generated as a test set for the community to develop and evaluate new detection algorithms, which are lacking in the field. The FDI's stealthy quality of the dataset is assessed and proven through a preliminary analysis based on both physical power law and statistical analysis.

Smart electricity meters as a prominent instance of the Internet of Things (IoT) have driven more efficient energy services in smart grids but also created growing concerns of consumer privacy. Homomorphic encryption of consumption data is a conventional solution for privacy protection, but it incurs a high computational burden to the resource-restrained smart meters (SMs) due to the encryption of high-frequency consumption readings. Perturbation is another major approach in providing the privacy protection of SM readings which is highly efficient. However, most existing perturbation-based works inadequately balance the tradeoff between the need of hiding individual consumption profiles and the need of retaining utility’s quality. This article proposes a hybrid privacy-preserving electricity consumption data publishing scheme without a trusted third party which utilizes the desirable properties of both perturbation and cryptography for better privacy-utility tradeoff and efficiency. The proposed scheme for fine-grained SM consumption data consists of two phases, which are noise generation and noise distribution. In the first phase, a distributed perturbation method is designed to provide differential privacy protection for high-frequency consumption while retaining the accuracy of energy services like regional load forecasting. In the second phase, a private noise distribution protocol,$nn$-PND, securely distributes$n$noise elements generated by an energy distribution operator to$n$SMs in a semihonest adversarial model. Formal proofs of correctness and privacy of the scheme are provided. Experiments of regional short-term electricity consumption forecast using real-world data sets demonstrate the preservation of the utility over the masked data of this scheme.

The advent of smart power grid, which plays a vital role in the upcoming smart city era, is accompanied with the implementation of a monitoring tool, called state estimation (SE). For the case of the unbalanced residential distribution grid, the state-estimating operation, which is conducted at a regional scale, is considered as an application of the edge computing-based Internet of Things (IoT). While the outcome of the SE is important to the subsequent control activities, its accuracy heavily depends on the data integrity of the information collected from the scattered measurement devices. This fact exposes the vulnerability of the SE module under the effect of data-driven attacks. Among these, the false data-injection (FDI) attack is attracting much attention due to its capability to interfere with the normal operation of the network without being detected. This article presents an attack design scheme based on a nonlinear physical-constraint model that is able to produce an FDI attack with theoretically stealthy characteristic. To demonstrate the effectiveness of the proposed design scheme, simulations with the IEEE 13-node test feeder and the WSCC 9-bus system are conducted. The experimental results indicate that not only the false-positive rate of the bad data detection mechanism is 100% but the physical consequence of the attack is severe. These results pose a serious challenge for the operators in maintaining the integrity of measurement data.

This Editorial Submission is for SS on Deep Learning and Data Analytics to Support the Smart Grid Operation with Renewable Energy. Out of 81 submissions in SS, only 12 have been finally accepted to be published in this Transactions.

This paper presents second-order cone programming (SOCP) and semidefinite programming (SDP) models to solve the multiperiod optimal control problem of unbalanced three-phase distribution grids with battery energy storage systems. The decision variables are the active and reactive power of the battery energy storage system. The objective is to minimize the power loss and energy purchase cost from the distribution substation. The optimal dispatch problem requires solution by volt/VAR optimization. The SOCP and SDP models ensure global optimum of original nonconvex nonlinear programming problem. Mupowerltiobjective volt/VAR optimization is performed and the benefits of reactive power support from battery energy storage systems are explored. A simulation-based heuristic to extract rank-one solution from the higher rank solution of SDP model is also proposed. Empirical results show the numerical stability and exactness of the proposed three-phase SOCP and SDP models.

This paper presents methods to find a positively invariant set (PIS) and derive conditions on the edge weights for a multirate Kuramoto oscillator network to achieve synchronization. These methods can be applied to a network with an arbitrary topology and nonidentical oscillators. The proposed methods are based on the construction of energy functions for this type of network. Two different conditions on the edge weights are provided by using graph spectral properties, or alternatively by analyzing a path set of a graph. These methods provide flexibility in checking the edge weight conditions. We further improve the estimate of the PIS for the multirate Kuramoto network when its damping coefficients are greater than a certain value. The effectiveness and conservativeness of the proposed methods are demonstrated by simulation studies. A comparison with another method from the existing literature shows that our method gives less conservative estimate of the PIS.

An optimal controller for high-precision spiral positioning of a piezoelectric tube scanner used in an atomic force microscope (AFM) is proposed in this paper. In the proposed control scheme, a second-order vibration compensator is incorporated with the piezoelectric tube scanner (PTS) to suppress the vibration of the PTS at the resonant frequency. An internal model of a reference sinusoidal signal is included with the augmented plant model and an integrator is introduced with a linear quadratic Gaussian controller which reduces the phase error between the input and output sinusoids. The proposed method allows a commercial AFM to scan at high scanning speeds as an alternative to the raster scanning approach. The performance of this controller is assessed with closed-loop frequency response, tracking accuracy, and a set of spiral scanned images. The raster scanned images obtained using the standard AFM PI controller is also presented for comparison with the spiral images. Experimental results prove the effectiveness of the proposed method.

Nanotechnology is the branch of science which deals with the manipulation of matters at an extremely high resolution down to the atomic level. In recent years, atomic force microscopy (AFM) has proven to be extremely versatile as an investigative tool in this field. The imaging performance of AFMs is hindered by: 1) the complex behavior of piezo materials, such as vibrations due to the lightly damped low-frequency resonant modes, inherent hysteresis, and creep nonlinearities; 2) the cross-coupling effect caused by the piezoelectric tube scanner (PTS); 3) the limited bandwidth of the probe; 4) the limitations of the conventional raster scanning method using a triangular reference signal; 5) the limited bandwidth of the proportional-integral controllers used in AFMs; 6) the offset, noise, and limited sensitivity of position sensors and photodetectors; and 7) the limited sampling rate of the AFM's measurement unit. Due to these limitations, an AFM has a high spatial but low temporal resolution, i.e., its imaging is slow, e.g., an image frame of a living cell takes up to 120 s, which means that rapid biological processes that occur in seconds cannot be studied using commercially available AFMs. There is a need to perform fast scans using an AFM with nanoscale accuracy. This paper presents a survey of the literature, presents an overview of a few emerging innovative solutions in AFM imaging, and proposes future research directions.

This paper presents a distributed multiagent scheme to detect and identify cyber threats on the protection systems of power grids. The integration of information and communication technologies into existing power grids builds critical cyber-physical energy systems, in which digital relays are networked cyber-physical components subject to various cyber threats. Cyber attacks on protection systems may mimic real faults, cause component failure, and disable the communication links. Agents utilize both cyber and physical properties to reinforce the detection technique and further distinguish cyber attacks from physical faults. This paper also introduces the problem of secure communication protocols and highlights the comparative studies for enhancing the security of the protection systems. The proposed scheme is validated using a benchmark power system under various fault and cyber attack scenarios.

This paper proposes a new cellphone application algorithm which has been implemented for the prediction of energy consumption at electric vehicle (EV) charging stations at the University of California, Los Angeles (UCLA). For this interactive user application, the total time for accessing the database, processing the data, and making the prediction needs to be within a few seconds. We first analyze three relatively fast machine learning-based time series prediction algorithms and find that the nearest neighbor (NN) algorithm (k NN with k = 1) shows better accuracy. Considering the sparseness of the time series of the charging records, we then discuss the new algorithm based on the new proposed time-weighted dot product (TWDP) dissimilarity measure to improve the accuracy and processing time. Two applications have been designed on top of the proposed prediction algorithm: one predicts the expected available energy at the outlet and the other one predicts the expected charging finishing time. The total time, including accessing the database, data processing, and prediction is approximately 1 s for both applications. The granularity of the prediction is 1 h and the horizon is 24 h; data have been collected from 20 EV charging outlets.

In this paper, a complex network framework based network resiliency (percolation) analysis has been presented. A topological model of transmission level Australian National Electricity Market (NEM) with projected renewable integration has been simulated. The effects of random and targeted removal of transmission lines or substations on the network structure and functionality have been analyzed. A fast and simple algorithm to analyze percolation on large scale power grid has also been addressed.

Being the most important critical infrastructure in Cyber-Physical Systems (CPSs), a smart grid exhibits the complicated nature of large scale, distributed, and dynamic environment. Taxonomy of attacks is an effective tool in systematically classifying attacks and it has been placed as a top research topic in CPS by a National Science Foundation (NSG) Workshop. Most existing taxonomy of attacks in CPS are inadequate in addressing the tight coupling of cyber-physical process or/and lack systematical construction. This paper attempts to introduce taxonomy of attacks of agent-based smart grids as an effective tool to provide a structured framework. The proposed idea of introducing the structure of space-time and information flow direction, security feature, and cyber-physical causality is innovative, and it can establish a taxonomy design mechanism that can systematically construct the taxonomy of cyber attacks, which could have a potential impact on the normal operation of the agent-based smart grids. Based on the cyber-physical relationship revealed in the taxonomy, a concrete physical process based cyber attack detection scheme has been proposed. A numerical illustrative example has been provided to validate the proposed physical process based cyber detection scheme.

This paper proposes a method to assess the practical stability of distribution systems with synchronous generators subject to changes in the system equilibrium conditions due to load variations. The concept of practical stability deals with two known state-space regions Ω1(which contains all the initial conditions reflecting the perturbations at which the system is subject during its operation) and Ω2(which represents the operating security region of the power distribution system) satisfying Ω1⊂ Ω2. The practical stability problem and the focus of this paper is to determine the minimum amount of time in which the system is required to stay in a particular loading condition, such that the system trajectories from an initial condition in Ω1will be confined in the security region of operation Ω2for the whole time interval of interest. This study was carried out using a mathematical model of the distribution system with synchronous generators in the form of a switched affine system. Sufficient conditions for the power distribution system with synchronous generators described as a switched affine system to be practically stable with respect to Ω1and Ω2are given in the form of matrix inequalities constraints. Numerical results were obtained for a model of a cogeneration plant of 10 MW added to a distribution network constituted by a feeder and six buses.

This paper presents a design of an internal reference model based optimal linear quadratic Gaussian controller (LQG) for a piezoelectric tube (PZT) actuator used in the atomic force microscope (AFM). This control scheme minimizes the steady-state error and provides improved closed-loop bandwidth and enables the controller to track reference triangular signal. The proposed scheme places the closed-loop poles in the left half of the s-plane such that significant damping of the resonant modes of the PZT actuator can be achieved. Experimental results with the AFM show that the proposed control approach is able to scan faster and improves the quality of images by minimizing blurring, tilting, and edge distortion of the image as compared to the existing PI controller of the AFM.

This paper presents the design of a model predictive control (MPC) scheme with a notch filter for reducing the tracking error of an atomic force microscope (AFM) by damping the resonant mode of the piezoelectric tube (PZT) scanner. The development of a controller for the AFM imaging and scanning speed is illustrated in this paper. Experimental results show that the proposed controller can increase the scanning speed significantly as compared with the existing PI controller.

This paper presents a hierarchical inner-outer loop-based scheme for flight control of a small unmanned helicopter in the presence of input time-delay. The controller is designed based on a two-time-scale separation architecture which includes a fast inner loop and a slow outer loop. The inner-loop (attitude controller) employs an inverse optimal control strategy, which circumvents the tedious task of numerically solving an online Hamilton-Jacobi-Bellman (HJB) equation to obtain the optimal controller. The designed controller is optimal with respect to a meaningful objective function which considers penalties for control input, angular position and angular velocity errors. The outer loop (position) controller uses the backstepping technique to control the position and keep the helicopter on track. Finally, computer simulations are conducted to validate the theoretical results and illustrate the tracking performance of the proposed control method.

This paper discusses a common approach to systems integration and automation for two different sizes of rotary wing UAVs. A linear model has been identified using time domain methods for both platforms. The identified models will help to evaluate parameters for designing nonlinear control systems for automatic landing of UAVs on moving platforms. In this paper, the innovation is the enhancement of experimental platforms to better suit experimental research in the design of controllers

In this paper, we address the design of an autopilot for autonomous landing of a helicopter on a rocking ship, due to rough sea. A tether is used for landing and securing a helicopter to the deck of the ship in rough weather. A detailed nonlinear dynamic model for the helicopter is used. This model is underactuated, where the rotational motion couples into the translation. This property is used to design controllers which separate the time scales of rotation and translation. It is shown that the tether tension can be used to couple the translation of the helicopter to the rotation. Two controllers are proposed in this paper. In the first, the rotation time scale is chosen much shorter than the translation, and the rotation reference signals are created to achieve a desired controlled behavior of the translation. In the second, due to coupling of the translation of the helicopter to the rotation through the tether, the translation reference rates are created to achieve a desired controlled behavior of the attitude and altitude. Controller A is proposed for use when the helicopter is far away from the goal, while Controller B is for the case when the helicopter is close to the ship. The proposed control schemes are proved to be robust to the tracking error of its internal loop and results in local exponential stability. The performance of the control system is demonstrated by computer simulations. Currently, work is in progress to implement the algorithm using an instrumented model of a helicopter with a tether.

In this paper, we address the design of an autopilot for autonomous landing of a helicopter on a rocking ship, due to rough sea. The deck is modeled to have a sinusoidal motion. The goal of the helicopter is to land on it during motion. In this work, we use a tether to help in target tracking. Based on the measurement of the angle between the cable and the helicopter/ship, a novel hierarchical two time-scale controller has been proposed to ensure landing of the helicopter on the ship. The system is demonstrated by computer simulation. Currently, work is under progress to implement the algorithm using an instrumented model of a helicopter using a tether.

A modified output consisting of the reflected tip position is analyzed. It is shown that, for an arbitrary beam, the transfer function from the base torque to the derivative of the modified output is passive when the hub inertia is very large compared to the beam inertia. In addition, it is shown that for a uniform beam, the same result also holds when the hub inertia is very small compared to the beam inertia. With the modified output, flexible beams can be easily controlled using quite simple control strategies such as PD control.>